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AI Analysis Guide
AI/ML is the technology for extracting value from data. This skill systematically covers all aspects of AI analysis — from machine learning fundamentals, deep learning, natural language processing, and computer vision to practical model development workflows.
Target Audience
- Engineers seeking to systematically learn AI/ML fundamentals
- Those working on data analysis and predictive model development
- Professionals looking to leverage AI in their work
Prerequisites
- Basic knowledge of Python
- Foundational mathematics (concepts in linear algebra and probability/statistics)
Learning Guide
00-fundamentals — AI/ML Fundamentals
01-ml-basics — Machine Learning Basics
02-deep-learning — Deep Learning
03-practical — Practical Applications
Quick Reference
ML Algorithm Selection:
Classification → Logistic Regression → Random Forest → XGBoost → NN
Regression → Linear Regression → Random Forest → XGBoost → NN
Clustering → k-means → DBSCAN → Hierarchical
Dimensionality → PCA → t-SNE → UMAP
Text → Transformer → BERT → GPT
Image → CNN → ResNet → Vision Transformer
References
- Goodfellow, I. et al. "Deep Learning." MIT Press, 2016.
- Géron, A. "Hands-On Machine Learning." O'Reilly, 2022.
- Vaswani, A. et al. "Attention Is All You Need." NeurIPS, 2017.
